Regions with cnn features (R-CNN) [1]} is probably the first detector showing that cnn can lead to dramatically higher object detection performance compared to traditional methods relying on hand-crafted features. Operating in the two-stage framework, R-CNN takes an image as input and outputs bounding boxes localizing the detected objects together with labels classifying them into some pre-defined object categories. The first stage aiming at producing a bunch of region proposals is carried out by the Selective Search algorithm [2]}; at a high level, the selective-search algorithm looks at the image through windows of different sizes, trying to group adjacent pixels by texture, color, or intensity to identify objects. Then, these proposals are warped to a fixed square size and passed through a cnn-based backbone, typically a modified version of AlexNet [3]} or of VGG-16 [4]}, in charge of performing the features extraction operation. Thus, the corresponding outputs are fixed-length feature vectors describing each region proposal. On the final layer of the detection pipeline, R-CNN adds a set of class-specific linear svm [5]} trained using these fixed-length features and having the goal of classifying them into pre-defined object categories. Furthermore, R-CNN runs a simple linear regression (inspired by [6]}) on the same fixed-length feature vectors to generate the final result, i.e., bounding box coordinates to fit the dimensions of the objects. An overview of this detection pipeline is shown in fig:rcnnarchitecture.
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